if not os.path.exists(result_dir):
os.makedirs(result_dir)
con = Config()
in_path = "./benchmarks/" + args.dataset + "/"
con.set_in_path(in_path)
test_file_path = ""
if args.test_file != "":
test_file_path = in_path + args.test_file
con.set_test_file_path(test_file_path)
con.set_work_threads(8)
con.set_train_times(args.num_epochs)
con.set_nbatches(args.nbatches)
con.set_alpha(args.learning_rate)
con.set_bern(1)
con.set_dimension(args.hidden_size)
con.set_lmbda(args.lmbda)
con.set_lmbda_two(0.01)
con.set_margin(1.0)
con.set_ent_neg_rate(args.neg_num)
con.set_opt_method(args.optim)
con.set_save_steps(args.save_steps)
con.set_valid_steps(args.valid_steps)
con.set_early_stopping_patience(10)
con.set_checkpoint_dir(checkpoint_dir)
con.set_result_dir(result_dir)
# knowledge graph completion ~ link prediction
con.set_test_link(True)
con.init()
# training mode
from config.Config import Config
from models.TransE import TransE
import tensorflow as tf
import numpy as np
import json
# (1) Set import files and OpenKE will automatically load models via tf.Saver().
con = Config()
con.set_in_path("./benchmarks/FB15K/")
con.set_test_flag(True)
con.set_work_threads(4)
con.set_dimension(50)
con.set_import_files("./res/model.vec.tf")
con.init()
con.set_model(TransE)
con.test()
# (2) Read model parameters from json files and manually load parameters.
# con = config.Config()
# con.set_in_path("./benchmarks/FB15K/")
# con.set_test_flag(True)
# con.set_work_threads(4)
# con.set_dimension(50)
# con.init()
# con.set_model(models.TransE)
# f = open("./res/embedding.vec.json", "r")
# content = json.loads(f.read())
# f.close()
# con.set_parameters(content)
# con.test()
def train_embeddings(opts):
os.makedirs(opts.output_dir, exist_ok=True)
if opts.model_name == "1hot":
embedding_dim, user_embeddings = one_hot_embedding(opts.openke_dir)
print("Successfully generated one-hot embeddings (dim: %s)" %
embedding_dim)
embedding_file = open(
os.path.join(opts.output_dir, "embedding.vec.txt"), "w")
embedding_file.write("{} {}\n".format(len(user_embeddings),
embedding_dim))
for user_id in user_embeddings:
embedding_file.write("{} {}\n".format(
user_id, " ".join([str(v) for v in user_embeddings[user_id]])))
embedding_file.close()
print("Saved one-hot embeddings to %s" % opts.output_dir)
return
if opts.model_name == "autoenc":
new_dim = 64
user_embeddings = autoencoder_embedding(opts.openke_dir, new_dim,
int(opts.nbatches),
int(opts.epochs))
print("Successfully generated autoencoder embeddings (dim: %s)" %
new_dim)
embedding_file = open(
os.path.join(opts.output_dir, "embedding.vec.txt"), "w")
embedding_file.write("{} {}\n".format(len(user_embeddings), new_dim))
for user_id in user_embeddings:
embedding_file.write("{} {}\n".format(
user_id, " ".join([str(v) for v in user_embeddings[user_id]])))
embedding_file.close()
print("Saved autoencoder embeddings to %s" % opts.output_dir)
return
if opts.model_name == "test":
new_dim = 64
import numpy as np
user_embedding_dim, user_embeddings_dict = one_hot_embedding(
opts.openke_dir)
user_ids, user_embeddings = zip(*list(user_embeddings_dict.items()))
user_embeddings = np.array(user_embeddings)
from pkg.test_model import TestModel
dae = TestModel([user_embedding_dim, new_dim * 2, new_dim])
genders, ages = PKG.get_userinfo(openke_dir=opts.openke_dir,
user_info_path=os.path.join(
opts.pkg_dir,
"user_id_imei_birth_gender.txt"),
user_ids=user_ids)
dae.train(x=user_embeddings,
y=np.array(genders) - 1,
epochs=int(opts.epochs),
batch_size=int(opts.nbatches))
user_embeddings = list(dae.encode(user_embeddings))
user_embeddings = dict(zip(user_ids, user_embeddings))
print("Successfully generated test embeddings (dim: %s)" % new_dim)
embedding_file = open(
os.path.join(opts.output_dir, "embedding.vec.txt"), "w")
embedding_file.write("{} {}\n".format(len(user_embeddings), new_dim))
for user_id in user_embeddings:
embedding_file.write("{} {}\n".format(
user_id, " ".join([str(v) for v in user_embeddings[user_id]])))
embedding_file.close()
print("Saved test embeddings to %s" % opts.output_dir)
return
from config.Config import Config
from models.TransE import TransE
from models.TransD import TransD
from models.TransH import TransH
from models.TransR import TransR
from models.RESCAL import RESCAL
from models.DistMult import DistMult
from models.ComplEx import ComplEx
from models.HolE import HolE
con = Config()
con.set_in_path(opts.openke_dir)
con.set_test_flag(True)
con.set_work_threads(4)
con.set_train_times(500)
con.set_nbatches(int(opts.nbatches))
con.set_alpha(float(opts.alpha))
con.set_margin(1.0)
con.set_bern(0)
con.set_dimension(64)
con.set_ent_neg_rate(1)
con.set_rel_neg_rate(0)
con.set_opt_method("Adam")
# Models will be exported via tf.Saver() automatically.
con.set_export_files(os.path.join(opts.output_dir, "model.vec.tf"), 0)
# Model parameters will be exported to json files automatically.
con.set_out_files(os.path.join(opts.output_dir, "embedding.vec.json"))
# Initialize experimental settings.
con.init()
# Set the knowledge embedding model
if opts.model_name == "TransE":
con.set_model(TransE)
elif opts.model_name == "TransD":
con.set_model(TransD)
elif opts.model_name == "TransR":
con.set_model(TransR)
elif opts.model_name == "TransH":
con.set_model(TransH)
elif opts.model_name == "ComplEx":
con.set_model(ComplEx)
elif opts.model_name == "DistMult":
con.set_model(DistMult)
elif opts.model_name == "HolE":
con.set_model(HolE)
elif opts.model_name == "RESCAL":
con.set_model(RESCAL)
else:
print("Unknown model: " + opts.model_name)
sys.exit(1)
# Train the model.
con.run()
# To test models after training needs "set_test_flag(True)".
con.test()